The objective of this proposal is to provide the applicant with exemplary training in image-based surgical planning and outcomes data collection, and to prepare the applicant for a career as an independent research scientist. To achieve this objective, a training plan within the scope of surgical treatment for the bicuspid aortic valve (BAV) has been developed. Aortic insufficiency (AI) is a common complication of BAV which until recently was always treated with aortic valve replacement surgery. Since BAV patients presenting with AI are typically young (20 to 50 years old), they are not ideal candidates for valve replacement because of concerns related to prosthesis durability and lifestyle restrictions associated with the need for anticoagulation. BAV repair is an emerging alternative treatment to valve replacement, but the surgical approach to BAV repair is in its infancy, and reports of long-term outcomes are scarce. Furthermore, it is often uncertain what the underlying mechanisms of AI are, since the surgeon must exam the valve intra-operatively when the heart is in an arrested state. Therefore, there are two unmet needs. The first need is for multicenter clinical outcomes data and the second is for technology that identifies the precise mechanism of AI in BAV repair candidates to facilitate patient-specific repair planning. The central hypothesis is that automated pre-operative 4D image analysis and visualization can reproducibly identify dynamic anatomical abnormalities causing AI and thereby augment intra-operative BAV inspection. The experiments proposed under this award are designed to: (1) develop and validate techniques for pre-operative multi-modal image analysis and visualization of the BAV, and test these capabilities in the operating room, (2) identify the mechanism of AI in BAV patients using pre- operative image analysis and visualization alone, and (3) establish an informatics platform for multi-institutional BAV repair outcomes data sharing. The proposed research will have a positive impact by initiating multicenter long-term data acquisition for BAV repair and by introducing unprecedented BAV analysis capabilities to the operating room. Ultimately, if successful, the research may lead to greater utilization of BAV repair, and reduce the need for reoperation for BAV-associated AI. Carrying out this original research will provide training in five areas: biomedical informatics, human computer interaction, leadership of multicenter studies, multi-modal imaging, and surgical planning. This training will be supplemented by didactic coursework, observational experience in the operating room, attendance at conferences and seminars, and training in the responsible conduct of research. The proposal will be carried out primarily at the Hospital of the University of Pennsylvania in collaboration with the University of Pittsburgh Medical Center and Stanford University School of Medicine. A multi-disciplinary team of experts in surgery, anesthesiology, biomedical informatics, and data storage and sharing will mentor the candidate. Ultimately, this training will provide the candidate with the foundation to lead a research program in image-based surgical planning and outcomes data collection and analysis.